class_docstr: Implements lit.Model interface for nearest neighbors.
  
  Returns the same output for every input.
class_docstr_tok: ["Implements","lit.Model","interface","for","nearest","neighbors",".","Returns","the","same","output","for","every","input","."]
code: def predict_minibatch(self, inputs: list[JsonDict], **kw):
      embs = [
          np.array([0, 0, 0, 0]),
          np.array([1, 1, 1, 0]),
          np.array([5, 8, -10, 0]),
      ]
      probas = np.array([0.2, 0.8])
      return [
          {"probas": probas, "input_embs": embs[i]}
          for i, _ in enumerate(inputs)
      ]
  
file_path: lit/lit_nlp/components/nearest_neighbors_test.py
imports: from absl.testing import absltest
  from lit_nlp.api import dataset
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.components import nearest_neighbors
  from lit_nlp.lib import caching
  from lit_nlp.lib import testing_utils
  import numpy
label_desc: Use image node_id 4 for calling the TestModelNearestNeighbors obj's underlying member method code with example usage: obj.predict_minibatch(inputs) and returns: unknown